Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations8512
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.9 MiB
Average record size in memory239.0 B

Variable types

Categorical3
Numeric9

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
feelslike is highly overall correlated with humidity and 1 other fieldsHigh correlation
gust is highly overall correlated with windHigh correlation
humidity is highly overall correlated with feelslike and 1 other fieldsHigh correlation
temperature is highly overall correlated with feelslike and 1 other fieldsHigh correlation
wind is highly overall correlated with gustHigh correlation
time is uniformly distributedUniform
cloud has 147 (1.7%) zerosZeros
precipitation has 6254 (73.5%) zerosZeros

Reproduction

Analysis started2024-11-18 14:37:58.628124
Analysis finished2024-11-18 14:38:07.993993
Duration9.37 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

time
Categorical

UNIFORM 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size449.0 KiB
00:00
1064 
03:00
1064 
06:00
1064 
09:00
1064 
12:00
1064 
Other values (3)
3192 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters42560
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00
2nd row03:00
3rd row06:00
4th row09:00
5th row12:00

Common Values

ValueCountFrequency (%)
00:00 1064
12.5%
03:00 1064
12.5%
06:00 1064
12.5%
09:00 1064
12.5%
12:00 1064
12.5%
15:00 1064
12.5%
18:00 1064
12.5%
21:00 1064
12.5%

Length

2024-11-18T21:38:08.165915image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T21:38:08.299285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
00:00 1064
12.5%
03:00 1064
12.5%
06:00 1064
12.5%
09:00 1064
12.5%
12:00 1064
12.5%
15:00 1064
12.5%
18:00 1064
12.5%
21:00 1064
12.5%

Most occurring characters

ValueCountFrequency (%)
0 22344
52.5%
: 8512
 
20.0%
1 4256
 
10.0%
2 2128
 
5.0%
3 1064
 
2.5%
6 1064
 
2.5%
9 1064
 
2.5%
5 1064
 
2.5%
8 1064
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22344
52.5%
: 8512
 
20.0%
1 4256
 
10.0%
2 2128
 
5.0%
3 1064
 
2.5%
6 1064
 
2.5%
9 1064
 
2.5%
5 1064
 
2.5%
8 1064
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22344
52.5%
: 8512
 
20.0%
1 4256
 
10.0%
2 2128
 
5.0%
3 1064
 
2.5%
6 1064
 
2.5%
9 1064
 
2.5%
5 1064
 
2.5%
8 1064
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22344
52.5%
: 8512
 
20.0%
1 4256
 
10.0%
2 2128
 
5.0%
3 1064
 
2.5%
6 1064
 
2.5%
9 1064
 
2.5%
5 1064
 
2.5%
8 1064
 
2.5%

month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3665414
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:08.436128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3670319
Coefficient of variation (CV)0.52886359
Kurtosis-1.186708
Mean6.3665414
Median Absolute Deviation (MAD)3
Skewness0.0054428773
Sum54192
Variance11.336904
MonotonicityNot monotonic
2024-11-18T21:38:08.551674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 744
8.7%
3 744
8.7%
5 744
8.7%
7 744
8.7%
8 744
8.7%
10 744
8.7%
4 720
8.5%
6 720
8.5%
9 720
8.5%
11 720
8.5%
Other values (2) 1168
13.7%
ValueCountFrequency (%)
1 744
8.7%
2 672
7.9%
3 744
8.7%
4 720
8.5%
5 744
8.7%
6 720
8.5%
7 744
8.7%
8 744
8.7%
9 720
8.5%
10 744
8.7%
ValueCountFrequency (%)
12 496
5.8%
11 720
8.5%
10 744
8.7%
9 720
8.5%
8 744
8.7%
7 744
8.7%
6 720
8.5%
5 744
8.7%
4 720
8.5%
3 744
8.7%

temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.364897
Minimum0
Maximum39
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:08.671490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q126
median28
Q331
95-th percentile34
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.339214
Coefficient of variation (CV)0.11772347
Kurtosis2.4211849
Mean28.364897
Median Absolute Deviation (MAD)2
Skewness0.098635732
Sum241442
Variance11.15035
MonotonicityNot monotonic
2024-11-18T21:38:08.802802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
26 1356
15.9%
27 1069
12.6%
25 1062
12.5%
28 714
8.4%
29 692
8.1%
30 676
7.9%
31 608
7.1%
32 597
7.0%
33 465
 
5.5%
24 386
 
4.5%
Other values (12) 887
10.4%
ValueCountFrequency (%)
0 5
 
0.1%
17 1
 
< 0.1%
20 8
 
0.1%
21 28
 
0.3%
22 56
 
0.7%
23 144
 
1.7%
24 386
 
4.5%
25 1062
12.5%
26 1356
15.9%
27 1069
12.6%
ValueCountFrequency (%)
39 5
 
0.1%
38 16
 
0.2%
37 46
 
0.5%
36 102
 
1.2%
35 202
 
2.4%
34 274
3.2%
33 465
5.5%
32 597
7.0%
31 608
7.1%
30 676
7.9%

feelslike
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.51351
Minimum20
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:08.930511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27
Q129
median32
Q336
95-th percentile40
Maximum46
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.094674
Coefficient of variation (CV)0.12593762
Kurtosis-0.7446137
Mean32.51351
Median Absolute Deviation (MAD)3
Skewness0.29325726
Sum276755
Variance16.766355
MonotonicityNot monotonic
2024-11-18T21:38:09.191549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
29 932
10.9%
30 881
10.4%
28 784
9.2%
31 744
 
8.7%
35 599
 
7.0%
32 578
 
6.8%
34 534
 
6.3%
33 530
 
6.2%
36 510
 
6.0%
38 471
 
5.5%
Other values (17) 1949
22.9%
ValueCountFrequency (%)
20 1
 
< 0.1%
21 4
 
< 0.1%
22 18
 
0.2%
23 19
 
0.2%
24 1
 
< 0.1%
25 75
 
0.9%
26 201
 
2.4%
27 377
4.4%
28 784
9.2%
29 932
10.9%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 1
 
< 0.1%
44 7
 
0.1%
43 7
 
0.1%
42 46
 
0.5%
41 153
 
1.8%
40 224
2.6%
39 346
4.1%
38 471
5.5%
37 468
5.5%

wind
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.279018
Minimum0
Maximum33
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:09.311959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q313
95-th percentile19
Maximum33
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7419651
Coefficient of variation (CV)0.46132473
Kurtosis0.21290275
Mean10.279018
Median Absolute Deviation (MAD)3
Skewness0.58650414
Sum87495
Variance22.486233
MonotonicityNot monotonic
2024-11-18T21:38:09.438380image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
8 861
 
10.1%
9 804
 
9.4%
10 753
 
8.8%
6 645
 
7.6%
12 608
 
7.1%
13 534
 
6.3%
7 523
 
6.1%
5 506
 
5.9%
11 468
 
5.5%
14 446
 
5.2%
Other values (23) 2364
27.8%
ValueCountFrequency (%)
0 9
 
0.1%
1 66
 
0.8%
2 108
 
1.3%
3 257
 
3.0%
4 373
4.4%
5 506
5.9%
6 645
7.6%
7 523
6.1%
8 861
10.1%
9 804
9.4%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 7
 
0.1%
26 15
0.2%
25 14
0.2%
24 30
0.4%
23 32
0.4%

direction
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size427.0 KiB
SE
1312 
WSW
1161 
SW
908 
ESE
865 
SSE
789 
Other values (11)
3477 

Length

Max length3
Median length3
Mean length2.3522086
Min length1

Characters and Unicode

Total characters20022
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENE
2nd rowNE
3rd rowNNE
4th rowNNE
5th rowENE

Common Values

ValueCountFrequency (%)
SE 1312
15.4%
WSW 1161
13.6%
SW 908
10.7%
ESE 865
10.2%
SSE 789
9.3%
E 455
 
5.3%
SSW 440
 
5.2%
W 424
 
5.0%
ENE 413
 
4.9%
NNE 397
 
4.7%
Other values (6) 1348
15.8%

Length

2024-11-18T21:38:09.575489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 1312
15.4%
wsw 1161
13.6%
sw 908
10.7%
ese 865
10.2%
sse 789
9.3%
e 455
 
5.3%
ssw 440
 
5.2%
w 424
 
5.0%
ene 413
 
4.9%
nne 397
 
4.7%
Other values (6) 1348
15.8%

Most occurring characters

ValueCountFrequency (%)
S 7042
35.2%
E 5883
29.4%
W 4734
23.6%
N 2363
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 7042
35.2%
E 5883
29.4%
W 4734
23.6%
N 2363
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 7042
35.2%
E 5883
29.4%
W 4734
23.6%
N 2363
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 7042
35.2%
E 5883
29.4%
W 4734
23.6%
N 2363
 
11.8%

gust
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.544878
Minimum0
Maximum55
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:09.701232image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19
median14
Q319
95-th percentile28
Maximum55
Range55
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2184705
Coefficient of variation (CV)0.49628952
Kurtosis0.95708449
Mean14.544878
Median Absolute Deviation (MAD)5
Skewness0.80991071
Sum123806
Variance52.106316
MonotonicityNot monotonic
2024-11-18T21:38:09.834149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12 530
 
6.2%
10 524
 
6.2%
13 507
 
6.0%
11 482
 
5.7%
14 478
 
5.6%
15 477
 
5.6%
9 458
 
5.4%
17 422
 
5.0%
8 405
 
4.8%
18 371
 
4.4%
Other values (39) 3858
45.3%
ValueCountFrequency (%)
0 7
 
0.1%
1 31
 
0.4%
2 76
 
0.9%
3 135
 
1.6%
4 163
 
1.9%
5 298
3.5%
6 268
3.1%
7 346
4.1%
8 405
4.8%
9 458
5.4%
ValueCountFrequency (%)
55 1
 
< 0.1%
51 2
 
< 0.1%
50 2
 
< 0.1%
45 5
0.1%
44 3
 
< 0.1%
43 4
< 0.1%
42 5
0.1%
41 8
0.1%
40 5
0.1%
39 4
< 0.1%

cloud
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.636396
Minimum0
Maximum99
Zeros147
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:09.976199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q118
median33
Q355
95-th percentile82
Maximum99
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation24.00555
Coefficient of variation (CV)0.63782808
Kurtosis-0.79091718
Mean37.636396
Median Absolute Deviation (MAD)17
Skewness0.47656808
Sum320361
Variance576.26644
MonotonicityNot monotonic
2024-11-18T21:38:10.128779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 247
 
2.9%
19 173
 
2.0%
18 163
 
1.9%
22 163
 
1.9%
16 157
 
1.8%
17 152
 
1.8%
25 151
 
1.8%
14 151
 
1.8%
20 150
 
1.8%
0 147
 
1.7%
Other values (90) 6858
80.6%
ValueCountFrequency (%)
0 147
1.7%
1 13
 
0.2%
2 35
 
0.4%
3 63
0.7%
4 89
1.0%
5 84
1.0%
6 77
0.9%
7 101
1.2%
8 119
1.4%
9 128
1.5%
ValueCountFrequency (%)
99 7
 
0.1%
98 7
 
0.1%
97 7
 
0.1%
96 7
 
0.1%
95 8
 
0.1%
94 10
0.1%
93 9
0.1%
92 13
0.2%
91 11
0.1%
90 22
0.3%

humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.228501
Minimum22
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:10.272335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile43
Q161
median74
Q384
95-th percentile91
Maximum96
Range74
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.878857
Coefficient of variation (CV)0.2088891
Kurtosis-0.35665786
Mean71.228501
Median Absolute Deviation (MAD)11
Skewness-0.62271755
Sum606297
Variance221.3804
MonotonicityNot monotonic
2024-11-18T21:38:10.418203image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 299
 
3.5%
86 293
 
3.4%
87 276
 
3.2%
84 268
 
3.1%
89 239
 
2.8%
82 238
 
2.8%
83 235
 
2.8%
88 226
 
2.7%
74 219
 
2.6%
72 209
 
2.5%
Other values (63) 6010
70.6%
ValueCountFrequency (%)
22 1
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 10
0.1%
29 9
 
0.1%
30 16
0.2%
31 14
0.2%
32 23
0.3%
33 17
0.2%
ValueCountFrequency (%)
96 6
 
0.1%
95 15
 
0.2%
94 44
 
0.5%
93 110
 
1.3%
92 113
1.3%
91 152
1.8%
90 175
2.1%
89 239
2.8%
88 226
2.7%
87 276
3.2%

precipitation
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52228618
Minimum0
Maximum9.9
Zeros6254
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:10.566581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile3.6
Maximum9.9
Range9.9
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation1.4033221
Coefficient of variation (CV)2.6868834
Kurtosis14.151921
Mean0.52228618
Median Absolute Deviation (MAD)0
Skewness3.5895835
Sum4445.7
Variance1.9693129
MonotonicityNot monotonic
2024-11-18T21:38:10.710858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6254
73.5%
0.1 304
 
3.6%
0.2 139
 
1.6%
0.3 129
 
1.5%
0.4 97
 
1.1%
0.5 89
 
1.0%
0.6 75
 
0.9%
0.7 72
 
0.8%
1 59
 
0.7%
0.9 56
 
0.7%
Other values (88) 1238
 
14.5%
ValueCountFrequency (%)
0 6254
73.5%
0.1 304
 
3.6%
0.2 139
 
1.6%
0.3 129
 
1.5%
0.4 97
 
1.1%
0.5 89
 
1.0%
0.6 75
 
0.9%
0.7 72
 
0.8%
0.8 54
 
0.6%
0.9 56
 
0.7%
ValueCountFrequency (%)
9.9 5
0.1%
9.8 3
< 0.1%
9.7 3
< 0.1%
9.6 4
< 0.1%
9.5 3
< 0.1%
9.4 3
< 0.1%
9.3 3
< 0.1%
9.2 2
 
< 0.1%
9.1 2
 
< 0.1%
9 4
< 0.1%

pressure
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009.5209
Minimum1002
Maximum1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.6 KiB
2024-11-18T21:38:10.829349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1006
Q11008
median1009
Q31011
95-th percentile1013
Maximum1018
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2508703
Coefficient of variation (CV)0.0022296421
Kurtosis-0.019042409
Mean1009.5209
Median Absolute Deviation (MAD)2
Skewness0.20562891
Sum8593042
Variance5.0664173
MonotonicityNot monotonic
2024-11-18T21:38:10.946960image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1009 1545
18.2%
1010 1396
16.4%
1008 1313
15.4%
1011 1105
13.0%
1007 813
9.6%
1012 784
9.2%
1006 484
 
5.7%
1013 474
 
5.6%
1014 217
 
2.5%
1005 175
 
2.1%
Other values (7) 206
 
2.4%
ValueCountFrequency (%)
1002 1
 
< 0.1%
1003 8
 
0.1%
1004 56
 
0.7%
1005 175
 
2.1%
1006 484
 
5.7%
1007 813
9.6%
1008 1313
15.4%
1009 1545
18.2%
1010 1396
16.4%
1011 1105
13.0%
ValueCountFrequency (%)
1018 2
 
< 0.1%
1017 14
 
0.2%
1016 41
 
0.5%
1015 84
 
1.0%
1014 217
 
2.5%
1013 474
 
5.6%
1012 784
9.2%
1011 1105
13.0%
1010 1396
16.4%
1009 1545
18.2%

weather
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size512.4 KiB
Partly cloudy
3654 
Clear
1103 
Sunny
1003 
Patchy rain possible
677 
Moderate or heavy rain shower
517 
Other values (15)
1558 

Length

Max length30
Median length29
Mean length12.624413
Min length4

Characters and Unicode

Total characters107459
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowClear
3rd rowSunny
4th rowSunny
5th rowPartly cloudy

Common Values

ValueCountFrequency (%)
Partly cloudy 3654
42.9%
Clear 1103
 
13.0%
Sunny 1003
 
11.8%
Patchy rain possible 677
 
8.0%
Moderate or heavy rain shower 517
 
6.1%
Cloudy 514
 
6.0%
Light rain shower 412
 
4.8%
Overcast 186
 
2.2%
Thundery outbreaks possible 126
 
1.5%
Torrential rain shower 114
 
1.3%
Other values (10) 206
 
2.4%

Length

2024-11-18T21:38:11.088077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cloudy 4168
24.1%
partly 3654
21.2%
rain 1857
10.8%
clear 1103
 
6.4%
shower 1043
 
6.0%
sunny 1003
 
5.8%
possible 803
 
4.6%
patchy 770
 
4.5%
light 551
 
3.2%
moderate 549
 
3.2%
Other values (12) 1770
10.2%

Most occurring characters

ValueCountFrequency (%)
y 10253
 
9.5%
l 9984
 
9.3%
r 9469
 
8.8%
a 8910
 
8.3%
8759
 
8.2%
o 7320
 
6.8%
t 6070
 
5.6%
u 5454
 
5.1%
e 5230
 
4.9%
d 4923
 
4.6%
Other values (21) 31087
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 10253
 
9.5%
l 9984
 
9.3%
r 9469
 
8.8%
a 8910
 
8.3%
8759
 
8.2%
o 7320
 
6.8%
t 6070
 
5.6%
u 5454
 
5.1%
e 5230
 
4.9%
d 4923
 
4.6%
Other values (21) 31087
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 10253
 
9.5%
l 9984
 
9.3%
r 9469
 
8.8%
a 8910
 
8.3%
8759
 
8.2%
o 7320
 
6.8%
t 6070
 
5.6%
u 5454
 
5.1%
e 5230
 
4.9%
d 4923
 
4.6%
Other values (21) 31087
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 10253
 
9.5%
l 9984
 
9.3%
r 9469
 
8.8%
a 8910
 
8.3%
8759
 
8.2%
o 7320
 
6.8%
t 6070
 
5.6%
u 5454
 
5.1%
e 5230
 
4.9%
d 4923
 
4.6%
Other values (21) 31087
28.9%

Interactions

2024-11-18T21:38:06.641149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.293059image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.162606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.079304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.034694image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.909244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.756063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.673327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.779035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.739782image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.399518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.259337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.172776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.126785image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.001222image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.862480image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.786387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.867120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.850829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.499458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.363883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.274455image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.230605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.108688image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.967683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.910404image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.970513image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.951699image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.591448image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.463858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.365273image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.321350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.196135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.072523image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.008663image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.065262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:07.065233image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.688747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.564150image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.561536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.418341image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.291342image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.170340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.106558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.161714image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:07.160219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.778018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.670199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.648881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.512626image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.378739image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.257542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.206468image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.250207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:07.260711image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.869064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.770733image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.736892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.602543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.464908image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.350268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.447834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.339162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:07.365674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:37:59.969655image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.874135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.838411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.710915image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.562990image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.446342image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.549766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.440216image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:07.464262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.061165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:00.972047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:01.929420image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:02.804945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:03.652220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:04.548539image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:05.646548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-18T21:38:06.537217image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-11-18T21:38:11.183905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
clouddirectionfeelslikegusthumiditymonthprecipitationpressuretemperaturetimeweatherwind
cloud1.0000.1140.0920.1260.1580.1570.423-0.3260.0720.0700.3340.054
direction0.1141.0000.1300.1710.1380.3200.0830.2030.1440.1690.1170.156
feelslike0.0920.1301.0000.014-0.631-0.0300.088-0.3070.9410.3180.1520.155
gust0.1260.1710.0141.0000.001-0.0880.258-0.3190.0870.1520.1860.941
humidity0.1580.138-0.6310.0011.0000.4030.168-0.155-0.7830.2890.170-0.189
month0.1570.320-0.030-0.0880.4031.0000.194-0.207-0.1770.0000.152-0.172
precipitation0.4230.0830.0880.2580.1680.1941.000-0.2990.0440.1150.3450.141
pressure-0.3260.203-0.307-0.319-0.155-0.207-0.2991.000-0.1980.1700.133-0.269
temperature0.0720.1440.9410.087-0.783-0.1770.044-0.1981.0000.3450.1830.248
time0.0700.1690.3180.1520.2890.0000.1150.1700.3451.0000.2360.128
weather0.3340.1170.1520.1860.1700.1520.3450.1330.1830.2361.0000.160
wind0.0540.1560.1550.941-0.189-0.1720.141-0.2690.2480.1280.1601.000

Missing values

2024-11-18T21:38:07.623610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T21:38:07.819919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timemonthtemperaturefeelslikewinddirectiongustcloudhumidityprecipitationpressureweather
000:00124.028.08.0ENE12.04.086.00.01012.0Clear
103:00123.027.08.0NE10.04.088.00.01011.0Clear
206:00123.026.08.0NNE11.07.085.00.01012.0Sunny
309:00128.033.011.0NNE13.06.064.00.01012.0Sunny
412:00131.035.010.0ENE12.062.053.00.01010.0Partly cloudy
515:00131.036.01.0NNE1.079.057.00.01009.0Cloudy
618:00126.029.05.0ESE10.040.077.00.01010.0Partly cloudy
721:00125.028.08.0NE15.029.079.00.21011.0Patchy rain possible
800:00124.026.010.0NNE17.012.084.00.01011.0Clear
903:00123.026.08.0NE14.017.085.00.01010.0Clear
timemonthtemperaturefeelslikewinddirectiongustcloudhumidityprecipitationpressureweather
850218:001130.034.09.0ESE14.079.069.00.11009.0Cloudy
850321:001127.030.08.0ESE12.047.077.00.11011.0Patchy rain possible
850400:001126.029.05.0NE8.022.079.00.01011.0Partly cloudy
850503:001126.028.04.0NNE5.021.082.00.01010.0Partly cloudy
850606:001126.028.03.0N4.024.082.00.01011.0Partly cloudy
850709:001129.032.04.0ENE5.017.068.00.01012.0Partly cloudy
850812:001133.037.05.0ENE5.015.052.00.01011.0Partly cloudy
850915:001134.038.03.0ENE4.019.050.00.01009.0Partly cloudy
851018:001131.034.06.0SE8.019.065.00.01009.0Partly cloudy
851121:001127.030.07.0SE10.033.075.00.01011.0Partly cloudy

Duplicate rows

Most frequently occurring

timemonthtemperaturefeelslikewinddirectiongustcloudhumidityprecipitationpressureweather# duplicates
006:00428.034.06.0SE7.019.075.00.01011.0Sunny2